Artificial Intelligence Adoption in Green Logistics: Implications for Sustainable Supply Chain Performance in Nigeria's Transportation Sector

Authors

  • Mohammed Rabiu Musa, PhD Department of Logistics and Maritime Economics, Faculty of Transportation Management, Federal University of Transportation Daura, Katsina State, Nigeria. Author https://orcid.org/0000-0002-6845-6106
  • Abdullahi Zakari Abdulhamid Department of Logistics and Maritime Economics, Faculty of Transportation Management, Federal University of Transportation Daura, Katsina State, Nigeria. Author https://orcid.org/0009-0002-8330-5020
  • Muhammad Nasir Hassan Directorate of Information and Communication Technology, Federal University of Transportation Daura, Katsina State, Nigeria. Author https://orcid.org/0009-0007-1739-5837
  • Yusuf Abubakar Department of Logistics and Maritime Economics, Faculty of Transportation Management, Federal University of Transportation Daura, Katsina State, Nigeria. Author https://orcid.org/0009-0006-2768-5216
  • Zubaida Abubakar Department of Logistics and Maritime Economics, Faculty of Transportation Management, Federal University of Transportation Daura, Katsina State, Nigeria. Author https://orcid.org/0009-0009-4591-1987
  • Saddam Saleh Legal Services Unit, Federal University of Transportation Daura, Katsina State, Nigeria. Author https://orcid.org/0009-0005-6889-0614

Abstract

Artificial Intelligence (AI) is increasingly transforming transportation and logistics by enabling data-driven decision-making, operational efficiency, and environmentally sustainable practices. However, the adoption of AI-enabled green logistics remains limited in Nigeria due to inadequate digital infrastructure, high implementation costs, limited technical expertise, weak institutional support, and regulatory challenges. This study examines how AI adoption influences green logistics practices and sustainable supply chain performance in Nigeria’s transportation and logistics sector. The study adopted a qualitative multiple-case study design involving four purposively selected organizations representing road freight and distribution, maritime and port-related logistics, third-party logistics, and integrated logistics services. Data were collected through semi-structured interviews with 24 key informants and analysed using thematic analysis following Braun and Clarke’s six-phase approach, with evidence examined at both within-case and cross-case levels. The findings indicate that AI applications, including predictive analytics, machine learning, intelligent route optimization, demand forecasting, and AI-enabled warehouse management, improve resource efficiency, supply chain visibility, fleet utilization, and operational responsiveness while reducing fuel consumption, waste, and environmental emissions. However, inadequate digital infrastructure, unreliable electricity, limited data quality, high implementation costs, cybersecurity concerns, and insufficient regulatory support constrain wider adoption. The study concludes that AI can strengthen green logistics and sustainable supply chain performance when supported by organisational readiness and enabling technological and institutional environments. It recommends investment in digital infrastructure, workforce capacity, AI governance, supportive policies, and public-private partnerships to accelerate sustainable logistics transformation and contribute to the achievement of Sustainable Development Goals (SDG) 9, 11, 12, 13, and 17 in Nigeria.

Keywords:

Artificial Intelligence, Green Logistics, Technology-Organization-Environment, Sustainable Supply Chain Performance, Sustainable Development Goals, Developing Economies, Sub-Saharan Africa

Downloads

Download data is not yet available.

Downloads

ACCESSES

DOI: 10.70382/ajaias.v13i2.0135
Views: 305  
Downloads: 73  

Published

2026-09-26

Issue

Section

Articles

How to Cite

Musa, M. R., Abdulhamid, A. Z., Hassan, M. N., Abubakar, Y., Abubakar, Z., & Saleh, S. (2026). Artificial Intelligence Adoption in Green Logistics: Implications for Sustainable Supply Chain Performance in Nigeria’s Transportation Sector. Journal of African Innovation and Advanced Studies, 13(2). https://doi.org/10.70382/ajaias.v13i2.0135

Share

PlumX

Most read articles by the same author(s)

Similar Articles

1-10 of 166

You may also start an advanced similarity search for this article.